Machine learning for cross-scale geomorphometric classification of landforms: a day at the beach

2018 ◽  
Author(s):  
Ashton Shortridge ◽  
Clayton Queen ◽  
Alan Arbogast
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Classification Accuracy ◽  
Spatial Prediction ◽  
Coastal Dune ◽  
Fine Scale ◽  
Coarse Scale ◽  
Sample Points

This paper investigates the use of random forests and spatial random forests (RFsp) for the classification of coastal dune areas along 41km of Lake Michigan’s shoreline using a lidar- derived DEM. Terrain variables across a range of spatial neighborhood scales are utilized, and for two different cell resolutions. Distance is explicitly incorporated into the RFsp models through the calculation of buffer distances around small numbers (6-13) of gridded points in the study area. While classification accuracy is high generally, RFsp produced much more accurate results. At the fine scale, topographic variables and their neighborhood ranges were not predictive of dune areas, perhaps because large (> 0.1 hectare) neighborhoods were not tested at that scale. At the coarse scale these variables were much more important. The use of small numbers of gridded (non-sample) points to improve spatial prediction warrants further investigation.

scholarly journals Machine learning for cross-scale geomorphometric classification of landforms: a day at the beach

2018 ◽  
Author(s):  
Ashton Shortridge ◽  
Clayton Queen ◽  
Alan Arbogast
Keyword(s):  
Machine Learning ◽  
Random Forests ◽  
Classification Accuracy ◽  
Spatial Prediction ◽  
Coastal Dune ◽  
Fine Scale ◽  
Coarse Scale ◽  
Sample Points

This paper investigates the use of random forests and spatial random forests (RFsp) for the classification of coastal dune areas along 41km of Lake Michigan’s shoreline using a lidar- derived DEM. Terrain variables across a range of spatial neighborhood scales are utilized, and for two different cell resolutions. Distance is explicitly incorporated into the RFsp models through the calculation of buffer distances around small numbers (6-13) of gridded points in the study area. While classification accuracy is high generally, RFsp produced much more accurate results. At the fine scale, topographic variables and their neighborhood ranges were not predictive of dune areas, perhaps because large (> 0.1 hectare) neighborhoods were not tested at that scale. At the coarse scale these variables were much more important. The use of small numbers of gridded (non-sample) points to improve spatial prediction warrants further investigation.

scholarly journals Multi-Class Assessment Based on Random Forests

2021 ◽  
Vol 11 (3) ◽  
pp. 92
Author(s):  
Mehdi Berriri ◽  
Sofiane Djema ◽  
Gaëtan Rey ◽  
Christel Dartigues-Pallez
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Random Forests ◽  
Learning Algorithm ◽  
Teaching Staff ◽  
Machine Learning Algorithm ◽  
Process Data ◽  
Training Courses ◽  
Education Courses

Today, many students are moving towards higher education courses that do not suit them and end up failing. The purpose of this study is to help provide counselors with better knowledge so that they can offer future students courses corresponding to their profile. The second objective is to allow the teaching staff to propose training courses adapted to students by anticipating their possible difficulties. This is possible thanks to a machine learning algorithm called Random Forest, allowing for the classification of the students depending on their results. We had to process data, generate models using our algorithm, and cross the results obtained to have a better final prediction. We tested our method on different use cases, from two classes to five classes. These sets of classes represent the different intervals with an average ranging from 0 to 20. Thus, an accuracy of 75% was achieved with a set of five classes and up to 85% for sets of two and three classes.

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

scholarly journals Machine learning for psychiatric patient triaging: an investigation of cascading classifiers

2018 ◽  
Vol 25 (11) ◽  
pp. 1481-1487 ◽  
Author(s):  
Vivek Kumar Singh ◽  
Utkarsh Shrivastava ◽  
Lina Bouayad ◽  
Balaji Padmanabhan ◽  
Anna Ialynytchev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Psychiatric Patient ◽  
Classification Accuracy ◽  
Psychiatric Patients ◽  
Patient Records ◽  
Classification Technique ◽  
Machine Learning Approach ◽  
The One ◽  
Unique Dataset

Abstract Objective Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by “I2B2 challenge,” a recent competition in the medical informatics community. Results The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes—the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system’s accuracy.

Enhanced Backpropagation Approach for Identifying Genetic Disease

2014 ◽  
Vol 622 ◽  
pp. 75-80
Author(s):  
Baskar Nisha ◽  
B. Madasamy ◽  
J.Jebamalar Tamilselvi
Keyword(s):  
Machine Learning ◽  
Genetic Disease ◽  
Classification Accuracy ◽  
Gene Selection ◽  
New Classification ◽  
Backpropagation Algorithm ◽  
High Classification Accuracy ◽  
Disease Associations ◽  
Disease Analysis

Classification of data on genetic disease is a useful application in microarray analysis. The genetic disease data analysis has the potential for discovering the diseased genes which may be the signature of certain diseases. Machine learning methodologies and data mining techniques are used to predict genetic disease associations of bio informatics data. Among numerous existing methods for gene selection, Backpropagation algorithm has become one of the leading methods and it gives less classification accuracy. It aims to develop a new classification algorithm (Enhanced Backpropagation Algorithm) for genetic disease analysis. Knowledge derived by the Enhanced Backpropagation Algorithm has high classification accuracy with the ability to identify the most significant genes.

scholarly journals RESEARCH ON BRAIN NETWORK CLASSIFICATION METHOD BASED ON INTEGRATED MODEL

2020 ◽  
pp. 1-2
Author(s):  
Zhang- sensen
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Brain Network ◽  
Classification Model ◽  
Comparative Experiment ◽  
Machine Learning Methods ◽  
Healthy Elderly ◽  
Healthy Elderly People ◽  
The Brain

mild cognitive impairment (MCI) is a condition between healthy elderly people and alzheimer's disease (AD). At present, brain network analysis based on machine learning methods can help diagnose MCI. In this paper, the brain network is divided into several subnets based on the shortest path,and the feature vectors of each subnet are extracted and classified. In order to make full use of subnet information, this paper adopts integrated classification model for classification.Each base classification model can predict the classification of a subnet,and the classification results of all subnets are calculated as the classification results of brain network.In order to verify the effectiveness of this method,a brain network of 66 people was constructed and a comparative experiment was carried out.The experimental results show that the classification accuracy of the integrated classification model proposed in this paper is 19% higher than that of SVM,which effectively improves the classification accuracy

scholarly journals A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6491
Author(s):  
Le Zhang ◽  
Jeyan Thiyagalingam ◽  
Anke Xue ◽  
Shuwen Xu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Classification Accuracy ◽  
Signal Amplitude ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Amplitude Change ◽  
Novel Method

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

scholarly journals Intelligent Fault Diagnosis of Rotary Machinery by Convolutional Neural Network with Automatic Hyper-Parameters Tuning Using Bayesian Optimization

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2411
Author(s):  
Davor Kolar ◽  
Dragutin Lisjak ◽  
Michał Pająk ◽  
Mihael Gudlin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Bayesian Optimization ◽  
Intelligent Fault Diagnosis ◽  
Accelerometer Signal ◽  
Rotary Machinery

Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

scholarly journals Automatic subtyping of individuals with Primary Progressive Aphasia

2020 ◽  
Author(s):  
Charalambos Themistocleous ◽  
Bronte Ficek ◽  
Kimberly Webster ◽  
Dirk-Bart den Ouden ◽  
Argye E. Hillis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Primary Progressive Aphasia ◽  
Support Vector ◽  
Progressive Aphasia ◽  
Primary Progressive ◽  
Machine Learning Model ◽  
Machine Learning Approach ◽  
Automated Machine Learning

AbstractBackgroundThe classification of patients with Primary Progressive Aphasia (PPA) into variants is time-consuming, costly, and requires combined expertise by clinical neurologists, neuropsychologists, speech pathologists, and radiologists.ObjectiveThe aim of the present study is to determine whether acoustic and linguistic variables provide accurate classification of PPA patients into one of three variants: nonfluent PPA, semantic PPA, and logopenic PPA.MethodsIn this paper, we present a machine learning model based on Deep Neural Networks (DNN) for the subtyping of patients with PPA into three main variants, using combined acoustic and linguistic information elicited automatically via acoustic and linguistic analysis. The performance of the DNN was compared to the classification accuracy of Random Forests, Support Vector Machines, and Decision Trees, as well as expert clinicians’ classifications.ResultsThe DNN model outperformed the other machine learning models with 80% classification accuracy, providing reliable subtyping of patients with PPA into variants and it even outperformed auditory classification of patients into variants by clinicians.ConclusionsWe show that the combined speech and language markers from connected speech productions provide information about symptoms and variant subtyping in PPA. The end-to-end automated machine learning approach we present can enable clinicians and researchers to provide an easy, quick and inexpensive classification of patients with PPA.

Sign in / Sign up

Export Citation Format

Share Document